Image Denoising with Convolutional Neural Networks for Percutaneous Transluminal Coronary Angioplasty

Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention

“…Regarding the CNN architecture, the same neural network structure as that employed in previous work 7 was used in this study. It is composed of a sequence of four convolutional layers, where each one includes a convolution layer followed by a detector layer with ReLU as activation function.…”

“…Convolutional neural networks (CNNs), 6 a specific type of artificial neural network widely used with image-based datasets, were employed to achieve the objectives. A preliminary study about simulated low x-ray dose image denoising was introduced 7 by creating simulated images containing artificial additive noise on high-dose PCI images. Poisson, Gaussian, and the mixture of both noises were investigated at different noise levels to evaluate the capabilities of designed CNNs for denoising simulated low-dose PCI images.…”

“…The specific contributions of this paper may be summarized as follows: first, compared with the results in the previous study, 7 the obtained results are improved by designing new loss functions for combining denoising with an edge-preserving effect on blood vessels. In addition, our proposed methods are compared with two state-of-the-art denoising methods to demonstrate the effectiveness of the proposed ones in terms of image quality and computation.…”

Deep Learning in Cardiology

“…Method Application/Notes a CT Lessman 2016 [195] CNN detect coronary calcium using three independently trained CNNs Shadmi 2018 [196] DenseNet compared DenseNet and u-net for detecting coronary calcium Cano 2018 [197] CNN 3D regression CNN for calculation of the Agatston score Wolterink 2016 [198] CNN detect coronary calcium using three CNNs for localization and two CNNs for detection Santini 2017 [199] CNN coronary calcium detection using a seven layer CNN on image patches Lopez 2017 [200] CNN thrombus volume characterization using a 2D CNN and postprocessing Hong 2016 [201] DBN detection, segmentation, classification of abdominal aortic aneurysm using DBN and image patches Liu 2017 [202] CNN left atrium segmentation using a twelve layer CNN and active shape model (STA13) de Vos 2016 [203] CNN 3D localization of anatomical structures using three CNNs, one for each orthogonal plane Moradi 2016 [204] CNN detection of position for a given CT slice using a pretrained VGGnet, handcrafted features and SVM Zheng 2015 [205] Multiple carotid artery bifurcation detection using multi-layer perceptrons and probabilistic boosting-tree Montoya 2018 [206] ResNet 3D reconstruction of cerebral angiogram using a 30 layer ResNet Zreik 2018 [207] CNN, AE identify coronary artery stenosis using CNN for LV segmentation and an AE, SVM for classification Commandeur 2018 [208] CNN quantification of epicardial and thoracic adipose tissue from non-contrast CT Gulsun 2016 [209] CNN extract coronary centerline using optimal path from computed flow field and a CNN for refinement CNN carotid intima media thickness video interpretation using two CNNs with two layers on Ultrasound Tom 2017 [226] GAN IVUS image generation using two GANs (IV11) Wang 2017 [227] CNN breast arterial calcification using a ten layer CNN on mammograms Liu 2017 [228] CNN CAC detection using CNNs on 1768 X-Rays Pavoni 2017 [229] CNN denoising of percutaneous transluminal coronary angioplasty images using four layer CNN Nirschl 2018 [230] CNN trained a patch-based six layer CNN for identifying heart failure in endomyocardial biopsy images Betancur 2018 [231] CNN trained a three layer CNN for obstructive CAD prediction from myocardial perfusion imaging a Results from these imaging modalities are not reported in this review because they were highly variable in terms of the research question they were trying to solve and highly inconsistent in respect with the use of metrics. Additionally all papers use private databases besides…”

“…The average diagnostic accuracies of the models reached to a maximum of 0.89 as the depth increased reaching eight layers; after that the increase in accuracy was limited. In [229] the authors created a method to denoise low dose percutaneous transluminal coronary angioplasty images. They tested mean squared error and structural similarity based loss functions on two patch-based CNNs with four layers and compared them for different types and levels of noise.…”